.png){kind=icon}
返回
A novel tolerance geometric method based on machine learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s10845-020-01706-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In most cases, designers must manually specify geometric tolerance types and values when designing mechanical products. For the same nominal geometry, different designers may specify different types and values of geometric tolerances. To reduce the uncertainty and realize the tolerance specification automatically, a tolerance specification method based on machine learning is proposed. The innovation of this paper is to find out the information that affects geometric tolerances selection and use machine learning methods to generate tolerance specifications. The realization of tolerance specifications is changed from rule-driven to data-driven. In this paper, feature engineering is performed on the data for the application scenarios of tolerance specifications, which improves the performance of the machine learning model. This approach firstly considers the past tolerance specification schemes as cases and sets up the cases to the tolerance specification database which contains information such as datum reference frame, positional relationship, spatial relationship, and product cost. Then perform feature engineering on the data and established machine learning algorithm to convert the tolerance specification problem into an optimization problem. Finally, a gear reducer as a case study is given to verify the method. The results are evaluated with three different machine learning evaluation indicators and made a comparison with the tolerance specification method in the industry. The final results show that the machine learning algorithm can automatically generate tolerance specifications, and after feature engineering, the accuracy of the tolerance specification results is improved.
Keyword:
Computer-aided tolerancing (CAT)
Tolerance specification
Machine learning
Feature engineering
Optimization problem
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.4
论文数:
3.5K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
分享收藏
An intelligent decision support system for production planning based on machine learning基于机器学习的生产计划智能决策支持系统
PREAI
分享收藏
查看更多内容